Figure 1. Map of the study area. (a) Köppen-Geiger climate
classification of the eastern Mediterranean (Atlas of Israel, 2011),
weather research and forecasting (WRF) model domains (D1-D3; Sect. 2.3),
and the range of the weather radar used for the identification of events
(Sect. 2.2). (b) Mean annual rainfall based on 1960-1990 interpolated
rain gauge data (Enzel et al., 2003), the innermost model domain, and
the weather radar range. Green and yellow colors, corresponding to drier
and wetter than 200 mm yr-1, respectively, roughly
mark the extent of the desert and Mediterranean climate regions. SND =
Sinai-Negev Desert, LM = Lebanon Mountains.
2.2 HPEs Identification
A collection of carefully selected HPEs was used in this study (Table S1
in the Supporting Information) following Armon et al. (2020) and
described here briefly. It consists of 41 HPEs identified based on their
magnitude compared to a 24-year rainfall climatology from
physically-corrected and gauge-adjusted weather radar rainfall data
(Marra & Morin, 2015; Fig. 1b). A HPE was identified when at least a
thousand 1-km2 radar pixels exhibited a rain amount
greater than the local 99.5th quantile of the non-zero
amounts for multiple durations, thus revealing events which can be
considered locally intense. To have a good representation of both short-
and long-duration HPEs, this process was repeated for durations of 1-72
h. Events identified as a HPE for more than one duration were merged
(Table S1). Return levels of the 99.5th quantile
thresholds are roughly 2-10 years. Events were separated by at least 24
h with less than 100 pixels displaying rainfall of more than 0.1 mm, and
they span 3.4 ± 1.6 d (mean and standard deviation). This collection of
HPEs represents a large variance of synoptic conditions, associated with
both MCs (35 events) and ARSTs (six events). A detailed description of
the collection of events and their rainfall characteristics is given in
Armon et al. (2020).
2.3 WRF Simulations
Each of the 41 HPEs was simulated twice, using version 3.9.1.1 of the
weather research and forecasting (WRF) model, at a convection permitting
resolution. The first simulation of each event represents the historic
conditions during the storm (near the end of the 20thcentury; sect. 2.3.1). Results of these simulations were previously
published (Armon et al., 2020). The second simulation represents a
hypothetic storyline in which the same HPE hits the area by the end of
the 21st century, when global warming conditions at a
representative concentration pathway (RCP) 8.5 prevail in the region
(sect. 2.3.2).
2.3.1 Simulation of Historic HPEs
Simulation of historic HPEs was conducted at a configuration suitable
for a skillful representation of rainfall patterns in the eastern
Mediterranean (Armon et al., 2020; Romine et al., 2013;
Rostkier-Edelstein et al., 2014; Schwartz et al., 2015). This
configuration includes three two-way nested domains (Fig. 1a; Table S2)
in which the innermost domain is simulated at a very high spatial and
temporal resolution (1 km2, 4-8 s). Convective
parametrization was used only in the two outer nests, while in the inner
nest the resolution is high enough to explicitly represent convection
(e.g. Prein et al., 2015). Further details are described in Table S2 and
in Armon et al. (2020).
Initial and boundary conditions for the historic simulations are
6-hourly ERA-Interim reanalysis data, at 60 vertical layers with a T255
spectral spatial resolution (~80 km) (Dee et al., 2011).
HPEs were simulated starting 24 h before the beginning of the observed
rainfall (rounded down to the previous 6 h) and lasted until the end of
the HPE (rounded to the following 6 h). The 24 h period before the event
is considered a spinup phase, for which we discard the rain fields. This
duration is considered long enough to correct spatial heterogeneities
arising from the initial conditions (Gómez-Navarro et al., 2019; Picard
& Mass, 2017; Warner, 2011: pp 215-216), which is crucial in correcting
non-physical properties of the atmosphere, expected to be present in the
PGW simulations because of the usage of ensemble mean fields (Shepherd,
2019; Tebaldi & Knutti, 2007) (Sect. 2.3.2). Precipitation outputs for
the innermost domain were saved at 10 min intervals.
As was shown by Armon et al. (2020), the total precipitation during most
of the HPEs reproduces the structure, location, and the seasonal change
of the precipitation’s center-of-mass of radar-observed precipitation,
albeit with a positive bias. Given that HPEs in the region are
characterized by small spatiotemporal scale rain-cells, it is important
to note the model’s skill is particularly good at its “raw” (1
km2) resolution for total rain amounts of
<25 mm, however, for larger amounts a skillful representation
must include a spatial averaging of at least a few tens of square
kilometers. The model also well represents areal mean rainfall amounts,
for various durations, which are crucial drivers of the hydrological
response to precipitation. MC-type HPEs are better simulated compared to
ARSTs, which are in general shorter and more local in nature.
2.3.2 Simulation of “Future” HPEs
To simulate the occurrence of the same HPEs in the future we used the
“pseudo global warming” (PGW) methodology (Kawase et al., 2009;
Rasmussen et al., 2011; Schär et al., 1996). Each of the simulated HPEs
was forced with the same input data as the historic events (Sect. 2.3.1)
after adding the signal of climate change to the following input
variables: surface pressure, skin temperature (including sea surface
temperature), and 3D fields of temperature, wind, and specific humidity.
In contrast to homogeneous changes common in the surrogate
climate-change methodology (Keller et al., 2018; Schär et al., 1996), 3D
spatial heterogeneity in the altered fields used in PGW experiments
allows for representation of non-uniform spatial response to global
warming (e.g., Rasmussen et al., 2011).
The changes applied over the initial and boundary conditions, for each
pixel and timestep (denoted hereon as \(\Delta\)), were derived from the
monthly values (Oct-Apr) of the ensemble mean of 29 models of the
Coupled Model Intercomparison Project phase 5 (CMIP5; Table S3) (K. E.
Taylor et al., 2012). They were based on the difference in the
corresponding parameter values for the end of the 21stcentury and the end of the 20th century under an RCP
8.5 scenario, as follows:
\(\Delta X_{j}=\left.\ \overset{}{X_{j}}\right|_{\mathbf{2074-2099}}^{29\ models}-\left.\ \overset{}{X_{j}}\right|_{\mathbf{1979-2004}}^{29\ models}\),
(1)
where \(X\) is a specified meteorological variable, defined for the
particular month (\(j\)) of the HPE occurrence. The double overbar
represents the mean of this parameter over the future (2074 to 2099) or
historic (1979 to 2004) periods, averaged among the 29 CMIP5 models.\(\Delta\) fields were linearly interpolated into a common grid, similar
to the ERA-Interim horizontal grid (T255) and consisting of 42 levels in
the vertical (model top = 10 hPa) for the 3D fields, over the entirety
of the outermost domain. The
changes applied represent a major warming of the region over the whole
troposphere, but specifically over its upper levels. Surface temperature
increases on average by 4.3°C. Alongside the warming, is a decrease in
the zonal component of wind and an increase of the sea level pressure in
the central Mediterranean, as detailed in the Supporting Information
Text S1 and Fig S1.
2.4 Analyzed Rainfall Parameters and Statistical Methods
The parameters examined here are based on the 10-min rainfall fields
from the innermost domain of both the historic and the “future” (PGW)
simulations. Rainfall parameters from the historic and future
simulations were compared both through their entire distribution across
all events and through an event-based paired comparison
(historic-future). To enable comparison between events of different
magnitudes, in many instances we normalize the quantity examined to its
historic value:\(100\times\frac{\overset{\overline{}}{\text{futur}e_{i}\ \ historic_{i}}}{\overset{\overline{}}{\text{histori}c_{i}}}\),
where \(i\) indicates a specific event for which “future” and
“historic” quantities are spatially averaged.
The following rainfall parameters were considered for each event:
(1) Rainfall accumulation for each pixel.
(2) Areal mean rainfall accumulation, which is the average of (1) over
the region of interest.
(3) Factors affecting areal mean rainfall accumulation: the value in (2)
above can be obtained by integrating rain rates over all rainy pixels
and timesteps and divide by the area of the region. Therefore, it is
possible to consider three factors affecting the areal mean
accumulation:
- The mean conditional rain rate is the average of rain rates
>0.1 mm h-1 over all timesteps and
pixels.
- The duration of the events is defined here as the time it took the
central 90% of rainfall mass to precipitate.
- The rain area of the event is the time-average of the area covered by
10-min rain rates >0.1 mm h-1 along the
event. We also examine the rain area for higher rain rate thresholds
in the range of 0.5-100 mm h-1.
(4) Maximal rain rates for durations of 10, 20 and 30 min, 1, 3, 6, 12
and 24 h for each pixel. To diminish the effect of single outlier
pixels, the rain field is first smoothed spatially using a 3X3 pixel
moving average window.
(5) Regionally maximal rain rates for the same durations as in (4),
which are taken as the maximal value from (4) over all pixels in the
region.
The analyses above were evaluated both over the entire study region and
over four sub-regions (Fig. 1b). These are the Mediterranean Sea area,
the land area, and a division of the latter to the area north to the 200
mm isohyet roughly corresponding to the Mediterranean climate zone, and
the area south of the 200 mm isohyet roughly corresponding to the desert
climate zone.
To analyze changes in the spatial structure of precipitation we compared
the spatial autocorrelation structure of the 10-min rain fields whenever
these were considered as having convective elements, following Marra and
Morin (2018). Convective elements are defined here as spatially
connected regions of area ≥3 km2 with rain rates
>10 mm h-1 that include at least one
pixel with rain rate >25 mm h-1.
Following Peleg et al. (2013), the spatial autocorrelation was
calculated through fitting a three-parameter exponential function to the
2D spatial autocorrelation field (e.g., Nerini et al., 2017) of each of
the convective rain fields as in Eq. 2:
\(r\left(h\right)=ae^{-\left(\frac{h}{b}\right)^{c}}\), (2)
where h is the lag distance, b , termed the correlation
distance, is the distance at which the correlation decreases to\(r=\text{ae}^{-1}\), a is the nugget (interception) parameter
and c is the shape parameter. For each event, the representative
parameters of Eq. (2) are the intra-event medians over all convective
rain fields. The comparison of the autocorrelation structure between
historic and future events is based on the inter-event medians of these
representative parameters.
Statistical significance of the changes in pixel- based parameters
is determined through the paired t-test with 5% significance level. For
event-based parameters, statistical significance is declared if both
paired t-test and paired Wilcoxon signed-rank tests are statistically
significant at the 5% level.
3 Results
To have a better understanding of the changes between “future” (PGW)
and historic simulations, we first present an examination of the first
HPE in our collection, which exhibits many of the features observed
throughout the events. It is followed by the results obtained throughout
the HPEs collection.
3.1 Case Study #1
The first HPE in our collection (2-5 Nov 1991) is characterized by the
passage of a MC, triggering numerous rain cells crossing the region with
a general SW-NE track. These rain cells contributed >100 mm
of accumulated rainfall mainly to the north coast and mountainous areas
of the study region (Fig. 2a, Movie S1). The areal average rainfall
accumulation simulated over the entire domain for the historic event is
21.9 mm. Compared to the historic event, the future event exhibits a
pronounced (-20%) decrease in precipitation with areal average rainfall
accumulation summing to 17.5 mm (Fig. 2b-c). The decrease is more
pronounced over the land area (-28%) compared to the sea area (-16%),
and is similar between the desert and Mediterranean regions of the land
area (-29% and -27%, respectively).
In contrast to the decrease in total rain amounts, short duration
(10-min) rain rates reveal a more complicated pattern (Fig. 2d). When
considering the distribution of all 10-min timesteps and pixels,
including those with no-rain (i.e., unconditional rain rates), most of
the distribution presents decreased rain rates and only the uppermost
quantiles (>99.75%) of future rain rates increase compared
to the historic ones. For example, the 99.99% quantile (corresponding
to ~1.4 104 pixel-timesteps values of
10-min rain rates), is increased by 21% (from 77 mm
h-1 to 93 mm h-1). However, the
decrease in most of the unconditional rain rate quantiles is very much
affected by the change in the spatiotemporal coverage of the event,
namely the wet-frequency. Conversely, considering the distribution of
the rainy pixels and timesteps, i.e., the conditional 10-min rain rate,
quantiles of the future HPE are increasing throughout the distribution
(Fig. 2d inset). The mean value of the conditional rain rate increases
from 2.64 mm h-1 for the historic event to 3.43 mm
h-1 for the future one (+30%).
In addition to the mean conditional rain rate, two other factors affect
the areal mean rainfall (Sect. 2.4), the duration and the rain area
(Fig. 2e). The duration of the event (Fig. S2a) decreased from 2440 min
to 1850 min (-24%) between the historic and future simulations. This
reduction reflects a delayed start of the “core” of the rainfall
during the passage of the MC, and an earlier termination (Movie S1). The
rain area (Fig. 2e) exhibits a major contraction (-38%) between the
historic and future simulations, from 31.9 103km2 (10.5% of the study region) to 19.7
103 km2 (6.5%) in historic and
future simulations, respectively. This major decrease in rain area
reflects the decrease in the area of precipitating rain cells, seen
clearly in Movie S1, as well as in their number. However, it is
important to note that we leave for future work a quantitative
assessment or tracking of individual rain cells (e.g., Belachsen et al.,
2017; Peleg & Morin, 2012). Nevertheless, we did compute the spatial
autocorrelation of convective rainfall (Sect. 2.4). The spatial
autocorrelation distance is 7 km and 5 km, respectively for the historic
and future events (Fig. S2b). In addition, the number of 10-min
convective timesteps decreases by 5.1% (from 429 to 407).
In summary, this case study of the first HPE in our collection indicates
that, moving from historic to future climates, areal mean rainfall
accumulation decreases whereas conditional 10-min rain rates increase.
This opposing behavior is caused by the decrease in the duration of the
rainfall and even a greater decrease in the rain area, where the latter
is probably due to the reduction in the area of precipitating rain cells
and possibly in their number. The decrease in duration and in rain area,
which means a decrease in wet-frequency, leads also to a decrease in
almost all quantiles (except the uppermost ones) of the unconditional
rain rate distribution, while the conditional rain rate distribution
presents an increase in all quantiles.